Review of Machine Learning Techniques for EEG Based Brain Computer Interface

2022 ◽  
Author(s):  
Swati Aggarwal ◽  
Nupur Chugh
Keyword(s):  
Machine Learning ◽  
Brain Computer Interface ◽  
Machine Learning Techniques ◽  
Computer Interface ◽  
Learning Techniques

scholarly journals Statistical Feature Extraction and Classification using Machine Learning Techniques in Brain-Computer Interface

2020 ◽  
Vol 9 (3) ◽  
pp. 1754-1758
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Brain Computer Interface ◽  
Essential Feature ◽  
Average Energy ◽  
Machine Learning Techniques ◽  
Computer Interface ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques

Brain Computer Interface is a paralyzed system. This system is used for direct communication between brain nerves and computer devices. BCI is an imagery movement of the patients who are all unable to communicate with the people. In EEG signals feature extraction plays an important role. Statistical based features are essential feature being used in machine learning applications. Researchers mainly focus on the filters and feature extraction techniques. In this paper data are collected from the BCI Competition III dataset 1a. Statistical features like minimum, maximum, standard deviation, variance, skewnesss, kurtosis, root mean square, average, energy, contrast, correlation and Homogeneity are extracted. Classification is done using machine learning techniques such as Support Vector Machine, Artificial Neural Network and K-Nearest Neighbor. In the proposed system 90.6% accuracy is achieved

Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface

2009 ◽  
pp. 113-135 ◽  
Author(s):  
Benjamin Blankertz ◽  
Michael Tangermann ◽  
Carmen Vidaurre ◽  
Thorsten Dickhaus ◽  
Claudia Sannelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Computer Interface ◽  
Mental States ◽  
Machine Learning Techniques ◽  
Computer Interface ◽  
Learning Techniques

scholarly journals A Comparative Study of Classification Techniques for P300 Speller

2020 ◽  
Vol 9 (7S) ◽  
pp. 102-106
Keyword(s):  
Machine Learning ◽  
Event Related Potential ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computer Interface ◽  
Support Vector ◽  
Classification Algorithms ◽  
Linear Discriminant ◽  
P300 Speller ◽  
Learning Techniques

P300 speller in Brain Computer Interface (BCI) allows locked-in or completely paralyzed patients to communicate with humans. To achieve the performance of characterization and increase accuracy, machine learning techniques are used. The study is about an event related potential (ERP) P300 signal detection and classification using various machine learning algorithms. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) are used to classify P300 and Non-P300 signal from Electroencephalography (EEG) signal. The performance of the system is evaluated based on f1-score using BCI competition III dataset II. In our system, we used LDA and SVM classification algorithms. Both the classifiers gave 91.0% classification accuracy.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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